# Interactive Visual Pattern Search on Graph Data via Graph Representation Learning

arXiv: https://arxiv.org/abs/2202.09459 · HTML: https://mlsanity.com/paper/2202.09459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 86 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [Propagation Kernels](https://mlsanity.com/paper/1410.3314/) (2014)
- [Deep Graph Matching Consensus](https://mlsanity.com/paper/2001.09621/) (2020)
- [Directed Graph Convolutional Network](https://mlsanity.com/paper/2004.13970/) (2020)
- [Neural Subgraph Matching](https://mlsanity.com/paper/2007.03092/) (2020)
- [REGAL: Representation Learning-based Graph Alignment](https://mlsanity.com/paper/1802.06257/) (2018)
- [Structured Query-Based Image Retrieval Using Scene Graphs](https://mlsanity.com/paper/2005.06653/) (2020)
- [Neural Subgraph Isomorphism Counting](https://mlsanity.com/paper/1912.11589/) (2019)
- [Image-to-Image Retrieval by Learning Similarity between Scene Graphs](https://mlsanity.com/paper/2012.14700/) (2020)
- [A Deep Generative Model for Graph Layout](https://mlsanity.com/paper/1904.12225/) (2019)
- [Deep Graph Similarity Learning: A Survey](https://mlsanity.com/paper/1912.11615/) (2019)
- [DeepDrawing: A Deep Learning Approach to Graph Drawing](https://mlsanity.com/paper/1907.11040/) (2019)
- [Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach](https://mlsanity.com/paper/1808.00703/) (2018)
- [DDGK: Learning Graph Representations for Deep Divergence Graph Kernels](https://mlsanity.com/paper/1904.09671/) (2019)
- [GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models](https://mlsanity.com/paper/1810.01405/) (2018)
- [Integrating Prior Knowledge in Mixed Initiative Social Network Clustering](https://mlsanity.com/paper/2005.02972/) (2020)
- [What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization](https://mlsanity.com/paper/1710.04328/) (2017)
- [A Visual Analytics Framework for Contrastive Network Analysis](https://mlsanity.com/paper/2008.00151/) (2020)

## Built on by (most cited first, 1 of 1)

- [Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction](https://mlsanity.com/paper/2303.09590/) (2023)

## Abstract (arXiv)

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understanding their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100x speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.